Skip to main content
Log in

Compact preference representation and Boolean games

  • Published:
Autonomous Agents and Multi-Agent Systems Aims and scope Submit manuscript

Abstract

Game theory is a widely used formal model for studying strategical interactions between agents. Boolean games (Harrenstein, Logic in conflict, PhD thesis, 2004; Harrenstein et al., Theoretical Aspects of Rationality and Knowledge, pp. 287–298, San Francisco Morgan Kaufmann, 2001) yield a compact representation of 2-player zero-sum static games with binary preferences: an agent’s strategy consists of a truth assignment of the propositional variables she controls, and a player’s preferences are expressed by a plain propositional formula. These restrictions (2-player, zero-sum, binary preferences) strongly limit the expressivity of the framework. We first generalize the framework to n-player games which are not necessarily zero-sum. We give simple characterizations of Nash equilibria and dominated strategies, and investigate the computational complexity of the associated problems. Then, we relax the last restriction by coupling Boolean games with a representation, namely, CP-nets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Apt, K. R., Rossi, F., & Venable, K. B. (2005). CP-nets and Nash equilibria. In Elsevier (Ed.), Proceedings of Third International Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS’05), Singapore, Dec 13–16.

  2. Bhat, N., & Leyton-Brown, K. (2004). Computing Nash equilibria of action-graph games. In Proceedings of Conference on Uncertainty in Artificial Intelligence (UAI’04), pp. 35–42.

  3. Bonzon, E., Lagasquie-Schiex, M.-C., & Lang, J. (2006). Compact preference representation for Boolean games. In Ninth Pacific Rim International Conference on Artificial Intelligence (PRICAI’06), Vol. 4099, pp. 41–50. Springer-Verlag.

  4. Bonzon, E., Lagasquie-Schiex, M.-C., Lang, J., & Zanuttini, B. (2006). Boolean games revisited. In 17th European Conference on Artificial Intelligence (ECAI’06), pp. 265–269. Springer-Verlag.

  5. Boutilier, C. (1994). Toward a logic for qualitative decision theory. In Proceedings of International Conference on Principles of Knowledge Representation and Reasoning (KR’94), pp. 75–86.

  6. Boutilier C., Brafman R.I., Domshlak C., Hoos H.H., Poole D. (2004) CP-nets : A tool for representing and reasoning with conditional ceteris paribus preference statements. Journal of Artificial Intelligence Research 21: 135–191

    MATH  MathSciNet  Google Scholar 

  7. Boutilier, C., Brafman, R. I., Domshlak, C., Hoos, H. H., & Poole, D. (2004). Preference-based constrained optimization with CP-nets. Computational Intelligence, 20(2), 137–157. (Special issue on preferences).

    Google Scholar 

  8. Boutilier, C., Brafman, R. I., Hoos, H. H., & Poole, D. (1999) Reasoning with conditional ceteris paribus preference statements. In Proceedings of Uncertainty in Artificial Intelligence (UAI’99).

  9. Brafman R.I., Dimopoulos Y. (2004) Extended semantics and optimization algorithms for CP-networks. Computational Intelligence 20(2): 218–245

    Article  MathSciNet  Google Scholar 

  10. Chevaleyre, Y., Endriss, U., & Lang, J. (2006). Expressive power of weighted propositional formulas for cardinal preference modelling. In P. Doherty, J. Mylopoulos, & C. Welty (Eds.), Proceedings of the 10th International Conference on Principles of Knowledge Representation and Reasoning (KR’06), pp. 145–152. AAAI Press.

  11. Conitzer, V., & Sandholm, T. (2005). Complexity of (Iterated) Dominance. In Proceedings of the 6th ACM Conference on Electronic Commerce (EC’05), pp. 88–97.

  12. Dastani, M., & Harrenstein, P. (2006). Effectivity and noncooperative solution concepts. (2006). In Seventh Conference on Logic and the Foundations of Game and Decision Theory (LOFT’06).

  13. De Vos, M., & Vermeir, D. (1999). Choice logic programs and nash equilibria in strategic games. In J. Flum & M. Rodriguez-Artalejo (Eds.), Computer Science Logic (CSL’99), Vol. 1683, pp. 266–276.

  14. Dunne, P. E., & van der Hoek, W. (2004). Representation and complexity in Boolean games. In J. J. Alferes & J. A. Leite (Eds.), Proceedings of the Ninth European Conference on Logics in Artificial Intelligence (JELIA’04), Vol. LNCS 3229, pp. 347–359.

  15. Fischer F., Holzer M., Katzenbeisser S. (2006) The influence of neighbourhood and choice on the complexity of finding pure Nash equilibria. Information Processing Letters 99(6): 239–245

    Article  MathSciNet  Google Scholar 

  16. Foo, N., Meyer, T., & Brewka, G. (2004). LPOD answer sets and Nash equilibria. In M. Maher (Ed.), Proceedings of the 9th Asian Computer Science Conference (ASIAN’04), pp. 343–351. Chiang Mai, Thailand, Springer LNCS 3321.

  17. Garey M.R., Johnson D.S. (1979) Computers and intractability: A guide to the theory of NP-completeness. W.H. Freeman and Company, New York

    MATH  Google Scholar 

  18. Giunchiglia E., Lee J., Lifschitz V., McCain N., Turner H. (2004) Nonmonotonic causal theories. Artificial Intelligence 153: 49–104

    Article  MATH  MathSciNet  Google Scholar 

  19. Gilboa I., Kalai E., Zemel E. (1993) The complexity of eliminating dominated strategies. Mathematics of Operations Research 18(3): 553–565

    Article  MATH  MathSciNet  Google Scholar 

  20. Gottlob G., Greco G., Scarcello F. (2005) Pure Nash equilibria: Hard and easy games. Journal of Artificial Intelligence Research 24: 357–406

    MATH  MathSciNet  Google Scholar 

  21. Hansson, S. O. (2001). Preference logic. In D. Gabbay & F. Guenthner (Eds.), Handbook of Philosophical Logic, Vol. 4. pp. 319–393.

  22. Harrenstein, P. (2004). Logic in conflict. PhD thesis, Utrecht University.

  23. Harrenstein, P., van der Hoek, W., Meyer, J.-J., & Witteveen, C. (2001). Boolean games. In J. van Benthem (Ed.), Proceedings of the 8th International Conference on Theoretical Aspects of Rationality and Knowledge (TARK’01), Vol. (Theoretical Aspects of Rationality and Knowledge), pp. 287–298. San Francisco, Morgan Kaufmann.

  24. Hillas, J., & Kohlberg, E. (2002). Foundations of strategic equilibrium. In R. Aumann & S. Hart (Eds.), Handbook of Game Theory, Vol. 3, pp. 1598–1663. North-Holland.

  25. Kearns, M., Littman, M. L., & Singh, S. (2001). Graphical models for game theory. In Uncertainty in Artificial Intelligence (UAI’01).

  26. Koller, D., & Milch, B. (2001). Multi-agent influence diagrams for representing and solving games. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI’01), pp. 1027–1034.

  27. La Mura, P. (2000). Game networks. In Uncertainty in Artificial Intelligence (UAI’00), pp. 335–342.

  28. Lang, J. (2007). Voting and aggregation on combinatorial domains with structured preferences. Proceedings of the Twentieth Joint International Conference on Artificial Intelligence (IJCAI’07), pp. 1366–1371.

  29. Lang J., Liberatore P., Marquis P. (2003) Propositional independence–formula-variable independence and forgetting. Journal of Artificial Intelligence Research 18: 391–443

    MATH  MathSciNet  Google Scholar 

  30. Lang, J., & Marquis, P. (1998). Two forms of dependence in propositional logic: controllability and definability. In Proceedings of the National Conference on Artificial Intelligence (AAAI’98), pp. 268–273.

  31. Leyton-Brown, K., & Tennenholtz, M. (2003). Local-effect games. In International Joint Conferences on Artificial Intelligence (IJCAI’03), pp. 772–777.

  32. Lin F. (2001) On the strongest necessary and weakest sufficient conditions. Artificial Intelligence 128: 143–159

    Article  MATH  MathSciNet  Google Scholar 

  33. Lin, F., & Reiter, R. (1994). Forget it. In Proceedings of the National Conference on Artificial Intelligence (AAAI’94), pp. 154–159.

  34. Osborne, M. J. (2004). An introduction to game theory. Oxford University Press.

  35. Osborne, M. J., & Rubinstein, A. (1994). A course in game theory. MIT Press.

  36. Papadimitriou, C. (1994). Computational complexity. Addison-Wesley.

  37. Poole, D. (1997). The independent choice logic for modelling multiple agents under uncertainty. Artificial Intelligence, 94(1–2), 7–56. (Special issue on economic principles of multi-agent systems).

  38. Schoenebeck, G., & Vadhan, S. (2006). The computational complexity of Nash equilibria in concisely represented games. In Proceedings of Conference on Electronic Commerce (EC’06).

  39. van Benthem, J. (2005). Open problems in logic and games. In We Will Show Them! (1), pp. 229–264.

  40. van der Hoek W., Wooldridge M. (2005) On the logic of cooperation and propositional control. Artificial Intelligence 164(1–2): 81–119

    MATH  MathSciNet  Google Scholar 

  41. Zanuttini B. (2003) New polynomial classes for logic-based abduction. Journal of Artificial Intelligence Research 19: 1–10

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Elise Bonzon.

Additional information

This article is a revised and extended version of the two conference articles [4] and [3].

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bonzon, E., Lagasquie-Schiex, MC., Lang, J. et al. Compact preference representation and Boolean games. Auton Agent Multi-Agent Syst 18, 1–35 (2009). https://doi.org/10.1007/s10458-008-9040-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10458-008-9040-2

Keywords

Navigation